performance improvement
this mapping is faster for large volumes
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@ -388,7 +388,7 @@ class DADF5():
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p = np.where(f['mapping/cellResults/constituent'][:,c]['Name'] == str.encode(label))[0]
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if len(p)>0:
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u = (f['mapping/cellResults/constituent'][p,c]['Position'])
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u = (f['mapping/cellResults/constituent']['Position'][p,c])
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a = np.array(f[pa])
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if len(a.shape) == 1:
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a=a.reshape([a.shape[0],1])
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